Decomposition-based multiobjective evolutionary algorithm with an ensemble of neighborhood sizes
The multiobjective evolutionary algorithm based on decomposition (MOEA/D) has demonstrated superior performance by winning the multiobjective optimization algorithm competition at the CEC 2009. For effective performance of MOEA/D, neighborhood size (NS) parameter has to be tuned. In this letter, an...
| Main Authors: | , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
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2013
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/85254 http://hdl.handle.net/10220/16502 |
| _version_ | 1826130150657359872 |
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| author | Suganthan, P. N. Zhao, Shi-Zheng. Zhang, Qing Fu. |
| author2 | School of Electrical and Electronic Engineering |
| author_facet | School of Electrical and Electronic Engineering Suganthan, P. N. Zhao, Shi-Zheng. Zhang, Qing Fu. |
| author_sort | Suganthan, P. N. |
| collection | NTU |
| description | The multiobjective evolutionary algorithm based on decomposition (MOEA/D) has demonstrated superior performance by winning the multiobjective optimization algorithm competition at the CEC 2009. For effective performance of MOEA/D, neighborhood size (NS) parameter has to be tuned. In this letter, an ensemble of different NSs with online self-adaptation is proposed (ENS-MOEA/D) to overcome this shortcoming. Our experimental results on the CEC 2009 competition test instances show that an ensemble of different NSs with online self-adaptation yields superior performance over implementations with only one fixed NS. |
| first_indexed | 2024-10-01T07:51:48Z |
| format | Journal Article |
| id | ntu-10356/85254 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T07:51:48Z |
| publishDate | 2013 |
| record_format | dspace |
| spelling | ntu-10356/852542020-03-07T13:57:27Z Decomposition-based multiobjective evolutionary algorithm with an ensemble of neighborhood sizes Suganthan, P. N. Zhao, Shi-Zheng. Zhang, Qing Fu. School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering The multiobjective evolutionary algorithm based on decomposition (MOEA/D) has demonstrated superior performance by winning the multiobjective optimization algorithm competition at the CEC 2009. For effective performance of MOEA/D, neighborhood size (NS) parameter has to be tuned. In this letter, an ensemble of different NSs with online self-adaptation is proposed (ENS-MOEA/D) to overcome this shortcoming. Our experimental results on the CEC 2009 competition test instances show that an ensemble of different NSs with online self-adaptation yields superior performance over implementations with only one fixed NS. 2013-10-16T03:00:27Z 2019-12-06T16:00:28Z 2013-10-16T03:00:27Z 2019-12-06T16:00:28Z 2012 2012 Journal Article Zhao, S. Z., Suganthan, P. N., & Zhang, Q. F. (2012). Decomposition-based multiobjective evolutionary algorithm with an ensemble of neighborhood sizes. IEEE transactions on evolutionary computation, 16(3), 442-446. https://hdl.handle.net/10356/85254 http://hdl.handle.net/10220/16502 10.1109/TEVC.2011.2166159 en IEEE transactions on evolutionary computation |
| spellingShingle | DRNTU::Engineering::Electrical and electronic engineering Suganthan, P. N. Zhao, Shi-Zheng. Zhang, Qing Fu. Decomposition-based multiobjective evolutionary algorithm with an ensemble of neighborhood sizes |
| title | Decomposition-based multiobjective evolutionary algorithm with an ensemble of neighborhood sizes |
| title_full | Decomposition-based multiobjective evolutionary algorithm with an ensemble of neighborhood sizes |
| title_fullStr | Decomposition-based multiobjective evolutionary algorithm with an ensemble of neighborhood sizes |
| title_full_unstemmed | Decomposition-based multiobjective evolutionary algorithm with an ensemble of neighborhood sizes |
| title_short | Decomposition-based multiobjective evolutionary algorithm with an ensemble of neighborhood sizes |
| title_sort | decomposition based multiobjective evolutionary algorithm with an ensemble of neighborhood sizes |
| topic | DRNTU::Engineering::Electrical and electronic engineering |
| url | https://hdl.handle.net/10356/85254 http://hdl.handle.net/10220/16502 |
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